I suspect if you crunch the numbers, accidents are going to be above normal for a while after Covid-19 reopenings.

Anecdotally, I'm seeing people doing mind-blowingly stupid things on the roadways right now. It seems like people have forgotten how to drive. I suspect the issue is that people rely too much on other cars to cue them how to behave and the concentration is too low.

(It could also be that a constant accident rate cleans off the worst of the drivers with regularity as they get into accidents and then wind up out of circulation. I really hope that isn't why ... that would be really depressing.)

No they’re underrated. We all know the stats. Driving isn’t the safest activity. Having said that there’s a lot of wishful thinking that the current state of ML can do any better if we were to just put them on the roads today as-is.

I learned to drive a car when I was 13. My older cousin took me to warped tour, got hammered and told me I had to drive home. I didn’t know what a clutch was, let alone a stick shift. After stalling in the parking lot a couple of times, I managed to drive us from Long Beach all the way back to my parents house in Pasadena. Love to see an AI handle that cold start problem.

Self-driving cars could work more like a hive mind. Humans can share ideas, but not reflexes and motor memory. So we practice individually, and we're great at recognizing moving stuff, but we never get very good at avoiding problems that rarely happen to us.

And we know we shouldn't drive tired or angry or intoxicated but obviously it still happens.

Exactly. The way to improve performance on a lot of AI problems is to get past the human tendency to individualistic AI, where every AI implementation has to deal with reality all on its own.

As soon as you get experience-sharing - culture, as humans call it, but updateable in real time as fast as data networks allow - you can build an AI mesh that is aware of local driving conditions and learns all the specific local "map" features it experiences. And then generalises from those.

So instead of point-and-hope rule inference you get local learning of global invariants, modified by specific local exceptions which change in real time.

It seems to me that humans require and get orders of magnitude more training data than any existing machine learning system. High "frame rate", high resolution, wide angle, stereo, HDR input with key details focused on in the moment by a mobile and curious agent, automatically processed by neural networks developed by millions of years of evolution, every waking second for years on end, with everything important labelled and explained by already-trained systems. No collection of images can come close.